Democratizing AI: What It Means for Data Scientists and Businesses

Blog: Ortec Blog

Artificial Intelligence (AI) is a central theme in the future of Data Science. Tech companies, such as Microsoft, are aiming to enable all companies to use AI. They call this process AI Democratization. The fact that such a concept is in the talks is of great interest to many data scientists because standardizing and automating data processing means that more people would be able to apply it.

The ultimate goal is to make AI accessible for every application, every business process and every employee. You could even consider it a sort of data science facelift, involving major investments for tech companies, like Microsoft. Inspired by my recent visit to Microsoft Ignite, in this blog, I would like to explain how Microsoft is pursuing its bold ambition to democratize AI and how it might change my role as a Data Scientist and impact businesses. Let’s start by giving you a short summary of the three Microsoft pillars: AI apps and agents, Machine Learning, and Knowledge Mining.

These services consist of pre-trained models, ready to be used through an API. Applications such as chatbots, optical character recognition and face recognition can be easily built with no prior training necessary. It decreases the need for data science capabilities. However, it still requires software engineering because certain areas like architecture, custom business rules, security and scalability remain a challenge, since the API’s deliver just one piece of the puzzle.

Knowledge Mining– through Azure Cognitive Search

Azure Cognitive Search helps you build search engines based on your own content. This is potentially very useful in creating chatbots, website, the processing of large text files or other applications.

These dynamic changes towards a more AI-driven world have the potential to impact the future of data science jobs but could also bring some risk to those using it. As a data scientist, it needs to be a main priority of yours to stay updated on certain aspects of your field such as technology or a new theoretical breakthrough in – for example – the area of machine learning. There is no doubt that changes are on their way. Certain tasks that a Data Scientist used to perform will soon be automated – and some already are.

As for the changes that are well on the way, here are some tips for Data Scientists to re-strategize for the future:

Make use of the standard pre-trained models. They can save you a lot of time (and your company a lot of resources).

Don’t limit yourself to open source. Open source is good and develops fast. But, the big tech companies are making it easier to use and share their technology.

Familiarize yourself with tools that can help you professionalize, standardize and deploy your machine learning models. In addition to Azure ML and Databricks there are a lot of other tools out there.

Find your niche. When 80% of the work can be solved by off-the-shelf tools, you’ll need to focus on the 20% that needs a custom approach.

Deep learning and text mining are fields that require a lot of theoretical knowledge to be applied correctly. This is where a good data scientist can really make the difference.

As for the effects on businesses, democratizing AI enables major opportunities for smaller companies that can’t attract their own data science talent. It, however, keeps being all about solving business challenges with the support of AI or Data Science solutions. Creating impact with data science will – especially in future – not be a technical challenge, instead it’s about investing in multi-disciplinary teams that can translate problems into working solutions and will follow through with a project at every maturity level.

What to keep in mind as a business?

It is crucial that you can successfully translate business challenges into AI solutions. Multi-disciplinary teams (consisting of f.e. software engineers, data scientists, data engineers, business translations and operational experts) enable organizations to approach challenges from multiple angles.

Make sure those teams fully understand your operations: I have seen advanced technical solutions that delivered only little business value and simple solutions that made a big difference.

Machine learning will become easier but understanding how to translate business challenges into feasible data science solutions will not.

Train, test, and deploy, and the cycle continues. The data science process is an iterative one. Doing that efficiently requires a well setup architecture and – again – an interdisciplinary team.

If you would like to find out more about AI, please contact Ivo Fugers, Data Scientist and IBM Watson Expert at ORTEC Consulting, via ivo.fugers@ortec.com or +31 (0)88 678 3265 or via our website: www.ortec-consulting.com.